5 Steps to Leverage Drone Surveying for Shadow Analysis of Solar Power Plants

At solar power plants, shadows falling on panels can lead to reduced power generation and differences in output between systems. Causes of shading are varied and include surrounding trees, slopes, rows of mounting racks, utility poles, fences, adjacent buildings, and graded terrain. Simply walking the site may not fully capture the distribution of shadows across the entire plant or how shadow lengths change with the seasons and times of day. What helps is aerial inspection using drone surveying and shadow analysis based on the acquired terrain and layout information.

This article explains, for operations personnel at solar power plants, the basic workflow in five steps to leverage drone surveying for shading analysis. Rather than simply viewing aerial images, it is important to clarify the inspection objectives, standardize surveying conditions, isolate the causes of shading, and translate the findings into information that can be used to make maintenance and retrofit decisions.

Table of Contents

• Step 1: Clarify the purpose of the shadow analysis and the scope to be checked

• Step 2: Organize on-site conditions and shadow factors before drone surveying

• Step 3: Create a shooting plan to make it easier to document shadow conditions

• Step 4: Interpret where shadows occur and what causes them from the acquired data

• Step 5: Utilize analysis results in maintenance planning and renovation decision-making

• Summary: Shadow analysis improves accuracy by combining on-site verification and drone surveying

Step 1: Clarify the Purpose of the Shadow Analysis and the Scope to Be Checked

When conducting shadow analysis at a solar power plant, it is important to first clarify what you want to determine. If you only need to confirm whether there is shading, visual inspection in clear weather or aerial imagery can provide a reasonable understanding. However, whether you are trying to investigate the causes of reduced power generation, prioritize the removal or pruning of nearby trees, or assess risks before expansion or refurbishment will change the required accuracy of drone surveys, the area to be photographed, and the information that should be recorded.

For example, in an existing photovoltaic (PV) plant, if the output of some strings or inverter units is low, the purpose of shading analysis is to isolate the cause. In this case, it is important to overlay the areas showing a downward trend in generation data with the areas where shadows are actually cast and verify them. Rather than simply capturing images of the entire site, you need data that allows you to confirm the positional relationships of the rows, azimuth, tilt, and nearby obstacles where differences in generation occur.

Meanwhile, in shadow analysis before new installation or renovation, it is necessary to consider not only whether shadows are currently being cast but also how far they might extend depending on the season. Because the sun’s elevation changes with the seasons, an issue that is hard to see in summer can result in long shadows from surrounding trees and terrain in winter. Drone surveying makes it easy to capture site topography, panel layout, surrounding elevation differences, and obstacle locations, so it is effective for preparing the baseline information needed to assess shadow impacts.

The scope of the inspection should also be decided at an early stage. Whether you look only at the power plant site, or include surrounding woodlands, roads, adjacent land, buildings, and slopes will affect the flight plan and the required permit checks. The causes of shading are not necessarily located within the power plant site. Trees or buildings on adjacent land to the south or to the east and west, tall slopes left over from site development, transmission equipment, and on-site poles can also contribute to shading. Therefore, depending on the purpose of the analysis, consider whether to include areas beyond the site boundary in the inspection.

Also, clarifying who will use the shadow analysis results will make subsequent steps go more smoothly. The required presentation varies depending on whether the site manager will use them for daily inspections, the maintenance company will use them to set response priorities, the owner or client will be reported to, or the design team will consider retrofit proposals. For on-site staff, it is necessary to clearly show which sections are likely to be shaded and when. For managers, the results should convey the areas suspected to affect power generation, the locations that should be checked going forward, and the priority order of possible responses.

If you carry out drone surveying without clear objectives, problems such as “there are no photos from this angle,” “trees on adjacent land aren’t captured,” or “the site wasn’t photographed during times when shadows occur” are likely to arise later. Shadow analysis is not a task that can be completed using only the acquired images or survey data. It must be assessed in combination with power generation data, equipment drawings, layout plans, maintenance records, and on-site visual inspections. That is why it is important to decide the verification objectives and intended use cases at the initial stage, and to plan the drone survey by working backwards to identify the necessary information.

Step 2: Organize on-site conditions and shadow factors before drone surveying

To make the most of drone surveying for shadow analysis, organizing on-site conditions before flight is essential. While drones make it easy to inspect large areas from above in a short time, correctly interpreting the causes of shadows requires a lot of information that should be gathered on the ground. If you assume you'll understand everything just by looking at the images after shooting, it can take a long time later to separate the causes.

First, what we want to confirm is the layout information of the solar power plant. We need to identify the orientation of the panel rows, the racking height, the spacing between rows, the tilt angle, the panel installation area, access paths, fences, drainage facilities, on-site columns, and the locations of junction boxes and collection equipment. Because shadows extend from higher objects to lower ones, not only the arrangement of the panels themselves but also the heights and positional relationships of surrounding equipment affect shading. In particular, if there are elevation differences within the site, upper panel rows, slopes, or embankments of service roads can cast shadows on lower panel rows.

Next, organize the surrounding obstacles. Trees, bamboo groves, buildings, utility poles, transmission towers, signboards, fences, adjacent agricultural facilities, road structures, and the like can all cause shading. The impact of shadows depends on the obstacle’s height, its distance from the power plant, its azimuth, the season, and the time of day. Trees that seem distant when viewed on site can cast long shadows when the sun is low in winter. Conversely, low nearby obstacles may have only limited impact depending on the height and tilt of the panel surface. Listing surrounding factors before a drone survey clarifies which directions should be recorded in detail during imaging.

Terrain conditions are also important. Solar power plants may be installed not only on flat land but also on slopes, reclaimed land, former forest land, valley terrain, and sites where embankments and cuttings coexist. In such locations the terrain itself can be a cause of shading. If there is a slope on the south side or high terrain to the east or west, shadows in the morning and evening can extend a long way. Terrain data created by drone surveys is useful for grasping such elevation differences, but the appearance of the ground surface under trees and of small structures can vary depending on data acquisition conditions, so it is necessary to make assessments in conjunction with on-site inspection.

In shadow analysis, it is also necessary to record the weather conditions and the state of the day when the images were captured. If the sky is not clear, actual shadows are hard to discern, and on slightly overcast days the boundaries of shadows become indistinct. Rain or strong winds can make the flight itself difficult. Also, because the sun’s elevation changes with the seasons, the date of capture is important information. Recording the time of capture, weather, cloud cover, sun direction, and local visibility conditions makes it easier to make judgments when reviewing the images later.

If power output data or monitoring data are available, it is effective to check them before a drone survey. Looking at which sections have low output, whether the decline is skewed toward the morning or the evening, and whether there is seasonality can help narrow down areas suspected of shading. For example, if output is low only in the morning, obstacles on the east side may be involved; if the decline is pronounced in the afternoon, obstacles on the west side may be relevant. Of course, causes of decreased power output are not limited to shading — dirt, equipment faults, wiring issues, degradation, contact with vegetation, and so on should also be considered. Therefore, it is realistic to treat shadow analysis not as a way to definitively determine the cause but as a means to organize and prioritize suspicious factors.

Furthermore, safety checks related to drone flights are essential. At solar power plants, attention must be paid to mounting racks, fences, power lines, surrounding roads, workers, maintenance vehicles, animals, and areas where strong winds funnel through. If the site is large, the aircraft may become difficult for the pilot to see. Before flight, takeoff and landing locations, emergency landing sites, access control, notifications to relevant parties, flight altitude, and flight routes should be organized. Even if the filming is for use in shadow analysis, inadequate safety management makes it unsuitable for on-site operations.

Thorough advance preparation turns the outputs of drone surveying into practical documentation for investigating the causes of shadows, rather than mere aerial photographs. Compiling site conditions, equipment layout, surrounding obstructions, terrain, power generation trends, and safety conditions, and then developing a flight plan, is the first step to improving the accuracy and usefulness of shadow analysis.

Step 3: Plan your shoot to make it easier to record shadow conditions

In drone surveying for shadow analysis, how you plan the image acquisition greatly affects the usability of the results. Photographing a solar power plant from above can capture the overall layout, but to confirm how shadows occur you need to be aware of the capture time, shooting direction, flight altitude, image overlap, target area, and alignment with ground control. In particular, because shadows move over time, it is extremely important to know when an image was taken.

First, consider which time of day you want to check for shadows. Shadows that tend to cause problems at solar power plants are those that lengthen in the morning and evening, and those that occur only for certain periods due to surrounding obstacles or the spaces between rows. Around noon the sun altitude is high and shadows often appear short, so that time can be insufficient if you want to assess morning or evening effects. If power output data shows a noticeable drop in the morning, capture images in the morning; if the drop occurs in the afternoon, focus on the afternoon. However, when the sun altitude is low the shadows become longer, and the contrast between light and dark in images increases, which can make some areas difficult to see. Therefore, it is desirable to consider multiple times of day depending on your objective.

Next, consider seasonal differences. The length and direction of shadows change with the seasons. In winter, the sun's altitude is low and shadows tend to be longer, making it an important period for checking shadow risk. If you judge from images taken only in summer that there are no shadow problems, you may overlook winter effects. It is not always possible to photograph every site in multiple seasons, but it is important to record the season when images were taken and not to conclude year‑round impacts based solely on results from that period.

The imaging area should include not only the panel surfaces but also the surrounding areas that could cause shadows. In particular, trees, structures, and terrain with elevation differences to the south, east, and west can affect the panels even if they are located some distance away. If the imaging area is cropped right at the site boundary, you may end up with shadows visible in the images but not the objects that caused them. For shadow analysis, it is important to be able to confirm both the shadows themselves and the causative objects. Therefore, when setting the flight area, allow some margin by including the power plant’s perimeter.

Flight altitude and shooting angle should also be used appropriately. Shooting directly overhead is a method that makes it easy to confirm the layout of the panel rows and the spread of shadows in plan. On the other hand, shooting from an oblique angle can make it easier to grasp the heights of trees, slopes, mounting racks, and surrounding structures. When using images for shadow analysis, in addition to overhead images, recording oblique images that show the objects responsible can be helpful for explanations in reports. However, because oblique images can make it difficult to read positional relationships accurately, it is easier to organize if you separate images used as survey deliverables from images used for explanatory purposes.

Image overlap is also important. When reconstructing the overall terrain and layout with drone surveying, insufficient overlap between consecutive images can lead to poor continuity of the data. In particular, solar panels present repeated similar shapes and are prone to reflections and contrast differences, so image processing can produce errors or gaps. Rather than photographing only the panel surfaces uniformly, it is also effective to capture features that can serve as feature points—such as access paths, fences, terrain, and surrounding structures—so they appear in the images. If necessary, set up ground-visible control points or checkpoints to make it easier to cross-check with drawings or coordinates afterward.

When photographing to record shadows, pay attention to exposure and reflections. Solar panels readily reflect light, and depending on the shooting direction, blown-out highlights or crushed blacks can occur, making shadow boundaries difficult to see. At times or angles with strong reflections, tonal variations in the image may be mistaken for actual shadows. When shooting, check the same location from slightly different angles and altitudes and keep records that make it possible to distinguish whether a feature is a shadow or a reflection.

Also, records made at the time of capture are as important as the image data. Record the flight date, capture time, weather, coverage area, flight altitude, the coordinate system and reference used, the on-site shadow conditions you checked, and any observations by the operators. Looking at the images alone later, you may not be able to tell why they were taken at that time or which shadows you wanted to check. When multiple people handle the data, organize file and folder names so that the capture time and coverage are clear; this reduces rework during analysis and report writing.

A flight plan is not only for creating clean aerial images. It is preparation intended to make it easier to later examine where shadows occur, the time periods they occur, the causative objects, and the extent of their impact. By choosing time periods that match the objective, being aware of seasonal differences, capturing the surrounding areas as well, using both straight-down and oblique views as appropriate, and recording the capture conditions, drone surveying results become materials that are easy to use for shadow analysis.

Step 4: Interpret the locations and causes of shadows from the acquired data

When checking images and terrain data acquired by drone surveys, first identify where shadows appear. Determine whether a shadow falls over the entire panel, only some rows, only the ends of rows, or merely extends over walkways or the surrounding terrain. The mere presence of shadows does not immediately mean a large loss in power generation, but by organizing the shadow’s location, extent, time of day, and the equipment units affected, it becomes easier to judge the priority of responses.

The first thing to check is the overlap between shadows and the panel layout. Using aerial images or orthophotos makes it easier to see which rows across the plant are shaded. If shadows are not falling on the panel surfaces, you cannot immediately conclude there will be an impact on power generation. On the other hand, if band-like shadows appear on panel edges or on lower rows, they can potentially affect output at certain times of day. In particular, if the same row or the same circuit is repeatedly shaded, it is worth cross-checking this against the power generation data.

Next, narrow down the cause of the shadow. From the shadow’s shape, orientation, and location, organize candidates such as trees, rows of mounting racks, on-site columns, fences, slopes, and adjacent buildings. If the shadow is elongated, it is likely caused by a columnar object; if it is a broad planar shadow, trees, buildings, or terrain with elevation differences may be involved. However, avoid conclusively determining the cause from images alone. Because shadow direction changes with the sun’s position, you should check the capture time and cross-check with the positions of obstacles observed on site. Even if a shadow appears clear in drone survey imagery, it may turn out on the ground to have been caused by a different object.

If terrain data is available, check the relationship between elevation differences and shadows. On sloped solar power sites, upper-level ground, slopes, retaining walls, embankments, and cut faces can cast shadows. Also, even when row spacing appears adequate, the tilt of the ground can change the relative heights of the front and rear rows, making shadows more likely at certain times of day. Checking elevation information obtained from drone surveys makes it easier to understand elevation differences that are hard to see on plan views alone. If shadow occurrences are concentrated at points where the terrain changes, the terrain conditions may be partly responsible.

Shading by trees and vegetation is also important. At solar power plants, the growth of nearby trees and on-site weeds, vines, and shrubs can become sources of shading over time. Even trees that were not a problem at the time of construction can increase in height and branch spread after a few years and cast shadows on the panels. Drone surveys make it easy to check the distribution of trees and their distance to the panels from above, helping to identify candidate areas that may require felling or pruning. However, tree height and branch overhang can be difficult to judge from images alone, so on-the-ground verification should be combined as needed.

Shadows between rows of mounting structures should not be overlooked. During periods or times of low solar elevation, the mounting structures or panels in a front row can cast shadows on those in a rear row. This varies depending on site conditions, row spacing, mounting angle, and ground slope. When using drone surveys to check inter-row shadows, always record the date and time of the flight and make assessments on the assumption that the shadows were visible under those conditions. Whether the shadows occur only during specific periods or recur throughout the year should be examined with additional verification and simulation.

When evaluating the impact of shading, it is important to consider it in conjunction with the management units of the power generation equipment. At a power plant, management is divided into units such as panels, strings, combiner boxes, power conditioners, and monitoring units. Unless you confirm which management unit the visible shaded area belongs to, you cannot correlate it with power generation data. By overlaying an equipment layout map on images obtained from drone surveys, it becomes easier to compare the shaded area with trends in power output decline. If the shaded area and the output drop coincide, shading becomes a strong candidate for the cause. Conversely, if a decline occurs in an area where shading is not visible, other factors need to be investigated.

However, it is important not to determine the cause of reduced power output solely from shadow analysis. There are many factors that can cause a decrease in output at a solar power plant, including dirt on the panel surface, bird damage, fallen leaves, snow accumulation, equipment malfunctions, faulty wiring, problems at connection points, aging, and communication anomalies. Even if shadows are identified in a drone survey, whether they are the primary cause of the decline must be assessed together with generation data and on-site inspections. Shadow analysis is most useful when considered as material to visualize candidate causes and to help prioritize on-site responses.

When summarizing analysis results, organize the shadow location, candidate causes, the date and time the shadow appeared, the range of equipment suspected to be affected, and whether additional confirmation is required. In addition to marking the images, record in writing the conditions under which the shadow was observed so that misunderstandings among stakeholders are less likely. Overestimating the extent of a shadow can lead to unnecessary measures, while underestimating it can lead to overlooking reductions in power generation. When using data from drone surveys, it is important to separate and organize what was actually observed from what was inferred.

Step 5: Use analysis results to inform maintenance planning and retrofit decisions

The value of shadow analysis lies not only in detecting shadows but in applying the results to maintenance planning and retrofit decisions. If drone surveys can identify where shadows occur and the likely causes, it becomes easier to prioritize on-site responses. Instead of treating all shadows the same, it is important to distinguish between areas suspected of affecting power generation, areas prone to recurrence, and areas that require safety verification.

First, clarify the shadows that should be dealt with in the short term. Shadows directly cast on the panel surface by trees, shadows from overgrown weeds and branches, and shadows caused by on-site equipment or temporarily stored materials are items that are relatively easy to consider addressing promptly. In particular, because vegetation growth progresses seasonally, record the extent when shadows are confirmed and reflect it in weeding and pruning plans. Using drone survey images makes it easier to explain to stakeholders which areas' vegetation is causing the shadows.

Next, we outline shadows that require medium- to long-term responses. Growth of surrounding trees, adjacent buildings, shadows caused by topography, and inter-row shading due to mounting arrangement may not be resolved immediately. Such shadows need to be evaluated in terms of their impact on power generation, mitigation costs, rights/ownership issues, safety, and the feasibility of construction. For example, if trees on neighboring land are the cause, coordination with the owner may be required. If the cause is terrain or mounting arrangement, measures such as weeding or pruning alone may not suffice, and refurbishment or decisions about operational acceptability may be necessary.

When incorporating this into maintenance plans, it is effective to align the timing of shadow occurrence with inspection schedules. At power plants where shadows lengthen in winter, conducting focused inspections from autumn through winter makes it easier to detect problems. In locations where vegetation grows rapidly in summer, strengthening weed-control plans from the end of the rainy season through summer is effective. When carrying out regular drone surveys, capturing images under similar conditions each time makes it easier to compare changes in shadows and vegetation growth.

When used to inform retrofit decisions, the locations where shadows occur are examined in conjunction with equipment layout and power generation data. If the shaded area is limited and the impact on power generation is small, monitoring over time and regular inspections may be sufficient. Conversely, if shadows are repeatedly observed on the same equipment units and a relationship with reduced power generation is suspected, it is worth considering removing the cause or revising the equipment layout. The results of shadow analysis are useful as documentation to explain the need for retrofits and help build consensus among stakeholders.

When compiling the report, separate and describe the facts observed on site, the findings confirmed by drone surveying, candidate causes of the shading, and proposed measures going forward. For example, you can state as a fact that shading was observed in the imagery, but additional verification is required to conclude that the shading is the primary cause of the reduction in power generation. In the report, use expressions such as "impact is suspected," "further verification is desirable," and "correlation with power generation data is necessary" to avoid excessive certainty. This is important both for public materials and for internal sharing.

Also, the results of a shadow analysis are not something you produce once and then finish. The environment around a solar power plant changes. Trees grow, vegetation lengthens with the seasons, and new structures may appear nearby. On developed sites, changes in drainage conditions or ground stability can also alter surrounding management conditions. By retaining past drone survey data, you can compare with previous surveys and more easily determine whether the cause of a shadow is progressing or merely temporary.

When sharing with stakeholders, it is important not just to present technical analysis results as-is, but to translate them into expressions that can be used on site. In which section, at what times, and what is likely causing the observed shadows? What should be checked at the next inspection? Which issues require immediate action and which require continued monitoring? By presenting this information in a clear, actionable form, shadow analysis becomes directly linked to on-site operations.

Drone surveying is not a panacea for shadow analysis, but it is an effective means of taking an overview of an entire power plant and organizing the relationships among terrain, equipment layout, and the surrounding environment. It visualizes areas that are difficult to grasp by on-site visual inspection alone, and when combined with power generation data and maintenance records, it makes it easier to prioritize responses. The practical point for operational use is not just detecting shadows but turning the findings into materials that lead to concrete next steps.

Summary: Shadow analysis enhances accuracy by combining on-site verification and drone surveying

In shadow analysis of solar power plants, using drone surveying makes it easier to check panel layout, surrounding obstacles, terrain, elevation differences, and the distribution of shadows from a broad perspective. Especially for plants with large sites, those on slopes, those surrounded by trees, or plants where the causes of reduced power output are hard to see, aerial records provide valuable clues.

However, you should avoid definitively determining the impact of shadows based solely on images captured by drones. Shadows change with the time of capture and the season, and reflections or contrasts in the images can appear to be shadows. There are also many causes of reduced power generation other than shadows. Therefore, in shadow analysis it is important to be mindful of the sequence of clarifying objectives, confirming site conditions, planning the image capture, interpreting the acquired data, and incorporating the findings into maintenance and refurbishment.

First, decide what you want to determine with the shadow analysis. Next, organize in advance the surrounding trees, topography, equipment layout, and power generation data. Then, conduct drone surveys at times and over areas where shadows are most apparent, and interpret the locations where shadows occur and their possible causes from the acquired images and terrain data. Finally, compile the results into materials that can be used for maintenance planning, weeding and pruning, additional inspections, and retrofit decisions.

By streamlining this workflow, drone surveying becomes operational data that supports the operation and maintenance of solar power plants, rather than merely aerial photography. If shadow impacts can be identified early, it becomes easier to investigate the causes of reduced power generation and to prioritize maintenance actions. Drone surveying is especially valuable when you need to efficiently inspect large sites or understand the relationship between on-site elevation differences and shading.

To make shadow analysis of solar power plants more practical for on-site use, it is important to establish a system that handles imaging, surveying, organization of location information, and field records as a unified workflow. When incorporating drone surveying into routine inspections, maintenance reporting, and pre-renovation surveys, it is important not to focus solely on finding shadows, but to organize the findings as documentation that leads to next steps by cross-referencing power generation data and on-site inspections.